The motivation of this work lies in the lack of interpretability and explainability of artificial intelligence (AI) models in security and safety critical applications.
For instance, regular traffic signs and any physically realizable trigger modifications represent intended and hidden encoded classes in a classification AI model (e.g., a yellow sticky on top of a STOP traffic sign as a trigger changing the label from the intended STOP to the target 65 m/h traffic sign classes [1]}).
Our lack of understanding of how classes are encoded in AI models for classifying traffic signs poses a safety threat in self-driving cars because AI models can contain such injected triggers causing misclassification. In addition to the application-specific motivation, methods for explainable AI are motivated in general by regulatory agencies, end users, decision makers, and engineers as they provide utilities for bias detection, trust in predictions, suitability for deployment, debugging, and recourse [2]}, [3]}.
